AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
HDP-SAM is proposed, a prompt-free and parameter-efficient SAM adaptation framework for UAV forest imagery that leverages SAM’s pretrained mask priors and structural representations to enhance crown-level coherence and demonstrate the effectiveness of HDP-SAM for fine-grained tree-species segmentation in complex UAV forest scenes.
Fine-grained mapping of riparian vegetation is important for ecological monitoring, invasive species control, and ecosystem restoration. However, riparian plant communities often exhibit fragmented patches, broad transition zones and high visual similarity among classes, making stable species-level segmentation difficu...
KGF-Net achieves competitive segmentation and boundary-delineation performance while maintaining a compact computational profile, and provides spatially explicit information on plantation extent, patch configuration, and management boundaries, supporting regional plantation monitoring and forest-management applications...
Semantic segmentation of forest point clouds is pivotal for automated forest inventory but is severely constrained by the scarcity of high-quality real-world annotations. Although synthetic forest point clouds provide a scalable alternative for training, models trained on synthetic data often suffer from pronounced per...
Individual-tree-level information on crown distribution and morphology underpins forest inventory, biomass estimation, and carbon accounting. High-resolution Unmanned Aerial Vehicle (UAV) imagery resolves single-tree detail, but automated crown extraction remains difficult: fully supervised segmentation depends on cost...
Abstract. The development of high-performance, affordable UAVs has transformed vegetation monitoring, enabling observation of forest canopies at an unprecedented level of detail. UAV-derived datasets now provide high-fidelity structural and physiological information at the individual tree level across entire forest sta...
A novel semi-supervised framework for individual tree segmentation featuring a novel crown vertical semantic distillation with selective coupling module to integrate these heterogeneous data sources and introduces a mask-guided training mechanism for tree crown to resolve the issue of inconsistent prediction of tree cr...
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